{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Networks\n",
    "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('y_train: ', (49000,))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive forward pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "('difference: ', 2.2121476417505994e-08)\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around e-8\n",
    "print('Testing conv_forward_naive')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aside: Image processing via convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:11: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "data": {
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VgIXcz4Gyks9gbgxqyYH3tB7CsqZpTq0Bado5KfXhVDywtHxpeno8b8qI5YCP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z6fc3JywnA4XFmirNfr8eKLL9YlYoQZFmBoC/SFJZYSGvKsRbcmzK8UX7azTqUmmawfKfclDL0N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\nN2i32xwfH5u4N3HfnZycUCqVaLfbRkJDmC6Ji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Z/PEwgE+Mu//EsSfX20LZNSqYDH7yOb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\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Op43RsQPEQkGuXZ4kGI4wMTHB3NwcVosu3k/E4wQCAXpiERUA+sADD9DV4Cvf+Cr1eheb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dx1F6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\nJ8b/B3O1kmksldKUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8f43a2610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d//2:-d//2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
    "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_noax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_noax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_noax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_noax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_noax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_noax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_noax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive backward pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "('dx error: ', 1.159803161159293e-08)\n",
      "('dw error: ', 2.2471264748452487e-10)\n",
      "('db error: ', 3.37264006649648e-11)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around e-8 or less.\n",
    "print('Testing conv_backward_naive function')\n",
    "print('dx error: ', rel_error(dx, dx_num))\n",
    "print('dw error: ', rel_error(dw, dw_num))\n",
    "print('db error: ', rel_error(db, db_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max-Pooling: Naive forward\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "('difference: ', 4.1666665157267834e-08)\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be on the order of e-8.\n",
    "print('Testing max_pool_forward_naive function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max-Pooling: Naive backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "('dx error: ', 3.27562514223145e-12)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be on the order of e-12\n",
    "print('Testing max_pool_backward_naive function:')\n",
    "print('dx error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast layers\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "\n",
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 3.153414s\n",
      "Fast: 0.032914s\n",
      "Speedup: 95.807904x\n",
      "('Difference: ', 4.926407851494105e-11)\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 5.791478s\n",
      "Fast: 0.007891s\n",
      "Speedup: 733.940206x\n",
      "('dx difference: ', 1.949764775345631e-11)\n",
      "('dw difference: ', 3.681156828004736e-13)\n",
      "('db difference: ', 0.0)\n"
     ]
    }
   ],
   "source": [
    "# Rel errors should be around e-9 or less\n",
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing conv_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('Difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting conv_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
    "print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
    "print('db difference: ', rel_error(db_naive, db_fast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.228668s\n",
      "fast: 0.001435s\n",
      "speedup: 159.345905x\n",
      "('difference: ', 0.0)\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 0.223832s\n",
      "fast: 0.008022s\n",
      "speedup: 27.902012x\n",
      "('dx difference: ', 0.0)\n"
     ]
    }
   ],
   "source": [
    "# Relative errors should be close to 0.0\n",
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing pool_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting pool_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional \"sandwich\" layers\n",
    "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "('dx error: ', 9.591132621921372e-09)\n",
      "('dw error: ', 5.802391137330214e-09)\n",
      "('db error: ', 1.0146343411762047e-09)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "# Relative errors should be around e-8 or less\n",
    "print('Testing conv_relu_pool')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "('dx error: ', 1.5218619980349303e-09)\n",
      "('dw error: ', 2.702022646099404e-10)\n",
      "('db error: ', 1.451272393591721e-10)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "# Relative errors should be around e-8 or less\n",
    "print('Testing conv_relu:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three-layer ConvNet\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Remember you can use the fast/sandwich layers (already imported for you) in your implementation. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Initial loss (no regularization): ', 2.302586071243987)\n",
      "('Initial loss (with regularization): ', 2.306699462583766)\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (no regularization): ', loss)\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (with regularization): ', loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to the order of e-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 1.380104e-04\n",
      "W2 max relative error: 1.822723e-02\n",
      "W3 max relative error: 3.064049e-04\n",
      "b1 max relative error: 3.477652e-05\n",
      "b2 max relative error: 2.516375e-03\n",
      "b3 max relative error: 7.945660e-10\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "np.random.seed(231)\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "# Errors should be small, but correct implementations may have\n",
    "# relative errors up to the order of e-2\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    # print(param_name, param_grad_num.shape, grads[param_name].shape)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 30) loss: 2.414060\n",
      "(Epoch 0 / 15) train acc: 0.200000; val_acc: 0.137000\n",
      "(Iteration 2 / 30) loss: 3.096478\n",
      "(Epoch 1 / 15) train acc: 0.140000; val_acc: 0.086000\n",
      "(Iteration 3 / 30) loss: 2.263457\n",
      "(Iteration 4 / 30) loss: 2.090160\n",
      "(Epoch 2 / 15) train acc: 0.230000; val_acc: 0.092000\n",
      "(Iteration 5 / 30) loss: 1.825391\n",
      "(Iteration 6 / 30) loss: 1.914284\n",
      "(Epoch 3 / 15) train acc: 0.510000; val_acc: 0.162000\n",
      "(Iteration 7 / 30) loss: 1.780130\n",
      "(Iteration 8 / 30) loss: 1.603287\n",
      "(Epoch 4 / 15) train acc: 0.580000; val_acc: 0.184000\n",
      "(Iteration 9 / 30) loss: 1.223486\n",
      "(Iteration 10 / 30) loss: 1.698993\n",
      "(Epoch 5 / 15) train acc: 0.620000; val_acc: 0.178000\n",
      "(Iteration 11 / 30) loss: 1.011410\n",
      "(Iteration 12 / 30) loss: 1.059796\n",
      "(Epoch 6 / 15) train acc: 0.780000; val_acc: 0.239000\n",
      "(Iteration 13 / 30) loss: 1.037864\n",
      "(Iteration 14 / 30) loss: 0.773454\n",
      "(Epoch 7 / 15) train acc: 0.670000; val_acc: 0.224000\n",
      "(Iteration 15 / 30) loss: 0.599178\n",
      "(Iteration 16 / 30) loss: 0.760589\n",
      "(Epoch 8 / 15) train acc: 0.830000; val_acc: 0.222000\n",
      "(Iteration 17 / 30) loss: 0.798383\n",
      "(Iteration 18 / 30) loss: 0.444189\n",
      "(Epoch 9 / 15) train acc: 0.840000; val_acc: 0.195000\n",
      "(Iteration 19 / 30) loss: 0.382310\n",
      "(Iteration 20 / 30) loss: 0.583877\n",
      "(Epoch 10 / 15) train acc: 0.860000; val_acc: 0.209000\n",
      "(Iteration 21 / 30) loss: 0.429573\n",
      "(Iteration 22 / 30) loss: 0.256244\n",
      "(Epoch 11 / 15) train acc: 0.860000; val_acc: 0.214000\n",
      "(Iteration 23 / 30) loss: 0.388342\n",
      "(Iteration 24 / 30) loss: 0.438435\n",
      "(Epoch 12 / 15) train acc: 0.920000; val_acc: 0.206000\n",
      "(Iteration 25 / 30) loss: 0.118094\n",
      "(Iteration 26 / 30) loss: 0.124752\n",
      "(Epoch 13 / 15) train acc: 0.950000; val_acc: 0.203000\n",
      "(Iteration 27 / 30) loss: 0.116363\n",
      "(Iteration 28 / 30) loss: 0.127471\n",
      "(Epoch 14 / 15) train acc: 0.980000; val_acc: 0.205000\n",
      "(Iteration 29 / 30) loss: 0.101172\n",
      "(Iteration 30 / 30) loss: 0.066361\n",
      "(Epoch 15 / 15) train acc: 0.980000; val_acc: 0.201000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "# print(len(data['X_train']))\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=15, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "# ?Solver\n",
    "\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m8fbbbzN27Fiys7PJycmhb9++rFq1ijfffJPJkyezfv16amtrueWWW5gyZQrw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\nMhEREZHI/h9xrvU4pqmJwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8ba24fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the net\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.302596\n",
      "(Epoch 0 / 1) train acc: 0.089000; val_acc: 0.079000\n",
      "(Iteration 21 / 980) loss: 2.402807\n",
      "(Iteration 41 / 980) loss: 2.220826\n",
      "(Iteration 61 / 980) loss: 2.171733\n",
      "(Iteration 81 / 980) loss: 2.208269\n",
      "(Iteration 101 / 980) loss: 1.996704\n",
      "(Iteration 121 / 980) loss: 1.862949\n",
      "(Iteration 141 / 980) loss: 1.786708\n",
      "(Iteration 161 / 980) loss: 1.656088\n",
      "(Iteration 181 / 980) loss: 1.722357\n",
      "(Iteration 201 / 980) loss: 1.566311\n",
      "(Iteration 221 / 980) loss: 1.900024\n",
      "(Iteration 241 / 980) loss: 1.507039\n",
      "(Iteration 261 / 980) loss: 1.863725\n",
      "(Iteration 281 / 980) loss: 1.576757\n",
      "(Iteration 301 / 980) loss: 1.780693\n",
      "(Iteration 321 / 980) loss: 1.598458\n",
      "(Iteration 341 / 980) loss: 1.582006\n",
      "(Iteration 361 / 980) loss: 1.691063\n",
      "(Iteration 381 / 980) loss: 1.551033\n",
      "(Iteration 401 / 980) loss: 1.618268\n",
      "(Iteration 421 / 980) loss: 1.731630\n",
      "(Iteration 441 / 980) loss: 1.518653\n",
      "(Iteration 461 / 980) loss: 1.479227\n",
      "(Iteration 481 / 980) loss: 1.583759\n",
      "(Iteration 501 / 980) loss: 1.859897\n",
      "(Iteration 521 / 980) loss: 1.587789\n",
      "(Iteration 541 / 980) loss: 1.822216\n",
      "(Iteration 561 / 980) loss: 1.637329\n",
      "(Iteration 581 / 980) loss: 1.880431\n",
      "(Iteration 601 / 980) loss: 1.737417\n",
      "(Iteration 621 / 980) loss: 1.576992\n",
      "(Iteration 641 / 980) loss: 1.624040\n",
      "(Iteration 661 / 980) loss: 1.415160\n",
      "(Iteration 681 / 980) loss: 1.712199\n",
      "(Iteration 701 / 980) loss: 1.629066\n",
      "(Iteration 721 / 980) loss: 1.575482\n",
      "(Iteration 741 / 980) loss: 2.228678\n",
      "(Iteration 761 / 980) loss: 1.494206\n",
      "(Iteration 781 / 980) loss: 1.567994\n",
      "(Iteration 801 / 980) loss: 1.474888\n",
      "(Iteration 821 / 980) loss: 1.472499\n",
      "(Iteration 841 / 980) loss: 1.365106\n",
      "(Iteration 861 / 980) loss: 1.321657\n",
      "(Iteration 881 / 980) loss: 0.987379\n",
      "(Iteration 901 / 980) loss: 1.788934\n",
      "(Iteration 921 / 980) loss: 1.511504\n",
      "(Iteration 941 / 980) loss: 1.596703\n",
      "(Iteration 961 / 980) loss: 1.613825\n",
      "(Epoch 1 / 1) train acc: 0.446000; val_acc: 0.436000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bb4FLv7hXd3RERJ4WfSydiIgzpDuri852wrU7b9ebBIqIXHKyGOanYCqSMau7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76855ebf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. As proposed in the original paper [3], batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect the statistics of each feature channel to be relatively consistent both between different imagesand different locations within the same image. Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over both the minibatch dimension `N` and the spatial dimensions `H` and `W`.\n",
    "\n",
    "\n",
    "[3] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "('  Shape: ', (2, 3, 4, 5))\n",
      "('  Means: ', array([9.33463814, 8.90909116, 9.11056338]))\n",
      "('  Stds: ', array([3.61447857, 3.19347686, 3.5168142 ]))\n",
      "After spatial batch normalization:\n",
      "('  Shape: ', (2, 3, 4, 5))\n",
      "('  Means: ', array([ 0.0610569, -0.0451249, -0.015932 ]))\n",
      "('  Stds: ', array([1.02618378, 0.96241754, 1.00731258]))\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "('  Shape: ', (2, 3, 4, 5))\n",
      "('  Means: ', array([7.24357007, 6.72291657, 7.03351336]))\n",
      "('  Stds: ', array([4.26595256, 3.90798026, 4.28859798]))\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print('Before spatial batch normalization:')\n",
    "print('  Shape: ', x.shape)\n",
    "print('  Means: ', x.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', x.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization:')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization (nontrivial gamma, beta):')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "('  means: ', array([-0.07486696,  0.08207072,  0.05214071,  0.03632024]))\n",
      "('  stds: ', array([0.96993818, 1.03118426, 1.02820835, 1.0016052 ]))\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in range(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After spatial batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', a_norm.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('dx error: ', 2.721986061450212e-07)\n",
      "('dgamma error: ', 5.909587857500314e-12)\n",
      "('dbeta error: ', 3.2756370464786835e-12)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "#You should expect errors of magnitudes between 1e-12~1e-06\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group Normalization\n",
    "In the previous notebook, we mentioned that Layer Normalization is an alternative normalization technique that mitigates the batch size limitations of Batch Normalization. However, as the authors of [4] observed, Layer Normalization does not perform as well as Batch Normalization when used with Convolutional Layers:\n",
    "\n",
    ">With fully connected layers, all the hidden units in a layer tend to make similar contributions to the final prediction, and re-centering and rescaling the summed inputs to a layer works well. However, the assumption of similar contributions is no longer true for convolutional neural networks. The large number of the hidden units whose\n",
    "receptive fields lie near the boundary of the image are rarely turned on and thus have very different\n",
    "statistics from the rest of the hidden units within the same layer.\n",
    "\n",
    "The authors of [5] propose an intermediary technique. In contrast to Layer Normalization, where you normalize over the entire feature per-datapoint, they suggest a consistent splitting of each per-datapoint feature into G groups, and a per-group per-datapoint normalization instead. \n",
    "\n",
    "![Comparison of normalization techniques discussed so far](normalization.png)\n",
    "<center>**Visual comparison of the normalization techniques discussed so far (image edited from [5])**</center>\n",
    "\n",
    "Even though an assumption of equal contribution is still being made within each group, the authors hypothesize that this is not as problematic, as innate grouping arises within features for visual recognition. One example they use to illustrate this is that many high-performance handcrafted features in traditional Computer Vision have terms that are explicitly grouped together. Take for example Histogram of Oriented Gradients [6]-- after computing histograms per spatially local block, each per-block histogram is normalized before being concatenated together to form the final feature vector.\n",
    "\n",
    "You will now implement Group Normalization. Note that this normalization technique that you are to implement in the following cells was introduced and published to arXiv *less than a month ago* -- this truly is still an ongoing and excitingly active field of research!\n",
    "\n",
    "[4] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)\n",
    "\n",
    "\n",
    "[5] [Wu, Yuxin, and Kaiming He. \"Group Normalization.\" arXiv preprint arXiv:1803.08494 (2018).](https://arxiv.org/abs/1803.08494)\n",
    "\n",
    "\n",
    "[6] [N. Dalal and B. Triggs. Histograms of oriented gradients for\n",
    "human detection. In Computer Vision and Pattern Recognition\n",
    "(CVPR), 2005.](https://ieeexplore.ieee.org/abstract/document/1467360/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for group normalization in the function `spatial_groupnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial group normalization:\n",
      "('  Shape: ', (2, 6, 4, 5))\n",
      "('  Means: ', array([9.72505327, 8.51114185, 8.9147544 , 9.43448077]))\n",
      "('  Stds: ', array([3.67070958, 3.09892597, 4.27043622, 3.97521327]))\n",
      "After spatial group normalization:\n",
      "('  Shape: ', (2, 6, 4, 5))\n",
      "('  Means: ', array([ 0.21365112, -0.20355326, -0.07072075,  0.06062289]))\n",
      "('  Stds: ', array([1.02072104, 0.77018481, 1.08252781, 1.04752652]))\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 6, 4, 5\n",
    "G = 2\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "x_g = x.reshape((N*G,-1))\n",
    "print('Before spatial group normalization:')\n",
    "print('  Shape: ', x.shape)\n",
    "print('  Means: ', x_g.mean(axis=1))\n",
    "print('  Stds: ', x_g.std(axis=1))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones((1,C,1,1)), np.zeros((1,C,1,1))\n",
    "bn_param = {'mode': 'train'}\n",
    "\n",
    "out, _ = spatial_groupnorm_forward(x, gamma, beta, G, bn_param)\n",
    "out_g = out.reshape((N*G,-1))\n",
    "print('After spatial group normalization:')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out_g.mean(axis=1))\n",
    "print('  Stds: ', out_g.std(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial group normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_groupnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('dx error: ', 6.101187892533301e-09)\n",
      "('dgamma error: ', 5.0241584640805145e-12)\n",
      "('dbeta error: ', 3.2755138958779985e-12)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 6, 4, 5\n",
    "G = 2\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(1,C,1,1)\n",
    "beta = np.random.randn(1,C,1,1)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "gn_param = {}\n",
    "fx = lambda x: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "fg = lambda a: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "fb = lambda b: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_groupnorm_forward(x, gamma, beta, G, gn_param)\n",
    "dx, dgamma, dbeta = spatial_groupnorm_backward(dout, cache)\n",
    "#You should expect errors of magnitudes between 1e-12~1e-07\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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